Penilaian kepatuhan syariat islam dalam merekabentuk tanah perkuburan islam berkonsepkan taman teknologi

Tanah Perkuburan Islam di Malaysia telah mencapai banyak pembaharuan. Antaranya pembinaan Raudhatul Sakinah iaitu tanah perkuburan dalam taman. Pada peringkat awal, dua tanah perkuburan telah dijadikan tapak pembinaan iaitu Tanah Perkuburan Islam KL-Karak dan Tanah Perkuburan Islam Bukit Kiara yang diuruskan oleh Jabatan Agama Islam Wilayah Persekutuan (JAWI). Lanjutan dari itu sekumpulan penyelidik dari UTHM melakukan penambahbaikan melalui usaha merekabentuk Tanah Perkuburan Islam Berkonsepkan Taman Teknologi menggunakan dengan aplikasi Geographical Information System (GIS) sebagai nilaitambah dalam dalam proses pembinaan tanah perkuburan Islam yang lebih sistematik. Lokasi kajian ini terletak di Tanah Perkuburan Islam, Parit Raja, Batu Pahat. Perkembangan ini memerlukan memerlukan satu garis panduan yang jelas agar usaha yang dilakukan berada dalam ruang lingkup kepatuhan syariat Islam. Justeru kertas kerja ini dihasilkan bagi menilai kepa rekabentuk tanah perkuburan Islam berkonsepkan taman teknologi ini adalah selari dengan ketetapan syariat Islam. Pendekatan kajian ini menggunakan kaedah temubual, permerhatian dan kajian perpustakaan. Hasil dari analisis kajian, terdapat tiga aspek yang perlu diambilkira semasa merekabentuk tanah perkuburan berkonsepkan taman teknologi iaitu tujuan mengkebumikan jenazah, tujuan menziarahi kubur dan bentuk binaan di atas tapak perkuburan. Dapatan daripada kajian ini akan menjadikan rekabentuk Tanah Perkuburan berkonsepkan Taman Teknologi menepati syariat Islam, diterima serta dimanafaatkan oleh seluruh masyarakat Islam di Malaysia

Career: artificial learning control systems for performance critical applications

Issued as final reportNational Science Foundation (U.S.

Controlling a drone: Comparison between a based model method and a fuzzy inference system

International audienceThe work describes an automatically on-line self-tunable fuzzy inference system (STFIS) of a new configuration of mini-flying called XSF (X4 Stationnary Flyer) drone. A fuzzy controller based on on-line optimization of a zero order Takagi-Sugeno fuzzy inference system (FIS) by a back propagation-like algorithm is successfully applied. It is used to minimize a cost function that is made up of a quadratic error term and a weight decay term that prevents an excessive growth of parameters. Thus, we carried out control for the continuation of simple trajectories such as the follow-up of straight lines, and complex (half circle, corner, and helicoidal) by using the STFIS technique. This permits to prove the effectiveness of the proposed control law. Simulation results and a comparison with a static feedback linearization controller (SFL) are presented and discussed. We studied the robustness of the two controllers used in the presence of disturbances. We presented two types of disturbances, the case of a breakdown of an engine as well as a gust of wind

Experimental Results of Concurrent Learning Adaptive Controllers

Commonly used Proportional-Integral-Derivative based UAV flight controllers are often seen to provide adequate trajectory-tracking performance only after extensive tuning. The gains of these controllers are tuned to particular platforms, which makes transferring controllers from one UAV to other time-intensive. This paper suggests the use of adaptive controllers in speeding up the process of extracting good control performance from new UAVs. In particular, it is shown that a concurrent learning adaptive controller improves the trajectory tracking performance of a quadrotor with baseline linear controller directly imported from another quadrotors whose inertial characteristics and throttle mapping are very di fferent. Concurrent learning adaptive control uses specifi cally selected and online recorded data concurrently with instantaneous data and is capable of guaranteeing tracking error and weight error convergence without requiring persistency of excitation. Flight-test results are presented on indoor quadrotor platforms operated in MIT's RAVEN environment. These results indicate the feasibility of rapidly developing high-performance UAV controllers by using adaptive control to augment a controller transferred from another UAV with similar control assignment structure.United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N000141110688)National Science Foundation (U.S.). Graduate Research Fellowship Program (Grant 0645960)Boeing Scientific Research Laboratorie

Test bed for applications of heterogeneous unmanned vehicles

Abstract This article addresses the development and implementation of a test bed for applications of heterogeneous unmanned vehicle systems. The test bed consists of unmanned aerial vehicles (Parrot AR.Drones versions 1 or 2, Parrot SA, Paris, France, and Bebop Drones 1.0 and 2.0, Parrot SA, Paris, France), ground vehicles (WowWee Rovio, WowWee Group Limited, Hong Kong, China), and the motion capture systems VICON and OptiTrack. Such test bed allows the user to choose between two different options of development environments, to perform aerial and ground vehicles applications. On the one hand, it is possible to select an environment based on the VICON system and LabVIEW (National Instruments) or robotics operating system platforms, which make use the Parrot AR.Drone software development kit or the Bebop_autonomy Driver to communicate with the unmanned vehicles. On the other hand, it is possible to employ a platform that uses the OptiTrack system and that allows users to develop their own applications, replacing AR.Drone’s original firmware with original code. We have developed four experimental setups to illustrate the use of the Parrot software development kit, the Bebop Driver (AutonomyLab, Simon Fraser University, British Columbia, Canada), and the original firmware replacement for performing a strategy that involves both ground and aerial vehicle tracking. Finally, in order to illustrate the effectiveness of the developed test bed for the implementation of advanced controllers, we present experimental results of the implementation of three consensus algorithms: static, adaptive, and neural network, in order to accomplish that a team of multiagents systems move together to track a target. Keywords Test bed, heterogeneous vehicles, laboratory environment

Hybrid active force control for fixed based rotorcraft

Disturbances are considered major challenges faced in the deployment of rotorcraft unmanned aerial vehicle (UAV) systems. Among different types of rotorcraft systems, the twin-rotor helicopter and quadrotor models are considered the most versatile flying machines nowadays due to their range of applications in the civilian and military sectors. However, these systems are multivariate and highly non-linear, making them difficult to be accurately controlled. Their performance could be further compromised when they are operated in the presence of disturbances or uncertainties. This dissertation presents an innovative hybrid control scheme for rotorcraft systems to improve disturbance rejection capability while maintaining system stability, based on a technique called active force control (AFC) via simulation and experimental works. A detailed dynamic model of each aerial system was derived based on the Euler–Lagrange and Newton-Euler methods, taking into account various assumptions and conditions. As a result of the derived models, a proportional-integral-derivative (PID) controller was designed to achieve the required altitude and attitude motions. Due to the PID's inability to reject applied disturbances, the AFC strategy was incorporated with the designed PID controller, to be known as the PID-AFC scheme. To estimate control parameters automatically, a number of artificial intelligence algorithms were employed in this study, namely the iterative learning algorithm and fuzzy logic. Intelligent rules of these AI algorithms were designed and embedded into the AFC loop, identified as intelligent active force control (IAFC)-based methods. This involved, PID-iterative learning active force control (PID-ILAFC) and PID-fuzzy logic active force control (PID-FLAFC) schemes. To test the performance and robustness of these proposed hybrid control systems, several disturbance models were introduced, namely the sinusoidal wave, pulsating, and Dryden wind gust model disturbances. Integral square error was selected as the index performance to compare between the proposed control schemes. In this study, the effectiveness of the PID-ILAFC strategy in connection with the body jerk performance was investigated in the presence of applied disturbance. In terms of experimental work, hardware-in-the-loop (HIL) experimental tests were conducted for a fixed-base rotorcraft UAV system to investigate how effective are the proposed hybrid PID-ILAFC schemes in disturbance rejection. Simulated results, in time domains, reveal the efficacy of the proposed hybrid IAFC-based control methods in the cancellation of different applied disturbances, while preserving the stability of the rotorcraft system, as compared to the conventional PID controller. In most of the cases, the simulated results show a reduction of more than 55% in settling time. In terms of body jerk performance, it was improved by around 65%, for twin-rotor helicopter system, and by a 45%, for quadrotor system. To achieve the best possible performance, results recommend using the full output signal produced by the AFC strategy according to the sensitivity analysis. The HIL experimental tests results demonstrate that the PID-ILAFC method can improve the disturbance rejection capability when compared to other control systems and show good agreement with the simulated counterpart. However, the selection of the appropriate learning parameters and initial conditions is viewed as a crucial step toward this improved performance

Fourteen Years of Autonomous Rotorcraft Research at the Georgia Institute of Technology

Presented at the 2nd Asia-Australia Rotorcraft Forum and 4th International Basic Research Conference on Rotorcraft Technology, Tianjin, China, September 8–11, 2013.Copyright ©2013 by the authors, Published with Permission.This paper presents a brief history and description of capabilities of the Georgia Tech Unmanned Aerial Vehicle Research Facility, while extracting and summarizing many significant and applicable results produced in the last fourteen years. Twenty-six selected publications are highlighted, which are representative of the research conducted at GT-UAVRF since 2000. The papers are divided into three groups: 1) development of a fault-tolerant adaptive flight control system, 2) development of vision-based navigation and control algorithms, and 3) special applications. For each group, the research and results are described, with references to the relevant paper(s)